Learning to Generate Posters of Scientific Papers

Researchers often summarize their work in the form of posters. Posters provide a coherent and efficient way to convey core ideas from scientific papers. Generating a good scientific poster, however, is a complex and time consuming cognitive task, since such posters need to be readable, informative, and visually aesthetic. In this paper, for the first time, we study the challenging problem of learning to generate posters from scientific papers. To this end, a data-driven framework, that utilizes graphical models, is proposed. Specifically, given content to display, the key elements of a good poster, including panel layout and attributes of each panel, are learned and inferred from data. Then, given inferred layout and attributes, composition of graphical elements within each panel is synthesized. To learn and validate our model, we collect and make public a Poster-Paper dataset, which consists of scientific papers and corresponding posters with exhaustively labelled panels and attributes. Qualitative and quantitative results indicate the effectiveness of our approach.Comment: in Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 201

Semi-automated mobile television interactive application generation based on XHTML and Java ME

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 11/02/2011.Mobile Digital TV (MDTV), the hybrid of Digital Television (DTV) and mobile devices (such as mobile phones), has introduced a new way for people to watch DTV and has brought new opportunities for development in the DTV industry. Nowadays, the development of the next generation MDTV service has progressed in terms of both hardware layers and software, with interactive services/applications becoming one of the future MDTV service trends. However, current MDTV interactive services still lack in terms of attracting the consumers and the service creation and implementation process relies too much on commercial solutions, resulting in most parts of the process being proprietary. In addition, this has increased the technical demands for developers as well as has increased substantially the cost of producing and maintaining MDTV services. In light of the aforementioned situation, the Thesis has contributed to this field, by proposing an innovative MDTV service creation and consumption system based on XHTML and Java ME. On the head-end it introduces a semi-automatic creation mechanism to facilitate a less technical and more efficient interactive service creation process. This enables designers and creative individuals to be actively involved in the MDTV service creation process and to develop interactive-rich MDTV service. On the client-end it employs an open-source software environment as the interactive service MDTV consumption platform, rendering the MDTV service implementation process as less proprietary as possible. Furthermore, the Thesis offers a discussion on the different MDTV interactive application models currently used and based on the proposed software, a novel MDTV service presentation method is further introduced and adopted instead of the Rich Media and ECMAScript based methods. Finally, a series of qualitative testing procedures have been implemented with regards to conducting an essential evaluation on the operability of the proposed software system

Automatically Discovering, Reporting and Reproducing Android Application Crashes

Mobile developers face unique challenges when detecting and reporting crashes in apps due to their prevailing GUI event-driven nature and additional sources of inputs (e.g., sensor readings). To support developers in these tasks, we introduce a novel, automated approach called CRASHSCOPE. This tool explores a given Android app using systematic input generation, according to several strategies informed by static and dynamic analyses, with the intrinsic goal of triggering crashes. When a crash is detected, CRASHSCOPE generates an augmented crash report containing screenshots, detailed crash reproduction steps, the captured exception stack trace, and a fully replayable script that automatically reproduces the crash on a target device(s). We evaluated CRASHSCOPE's effectiveness in discovering crashes as compared to five state-of-the-art Android input generation tools on 61 applications. The results demonstrate that CRASHSCOPE performs about as well as current tools for detecting crashes and provides more detailed fault information. Additionally, in a study analyzing eight real-world Android app crashes, we found that CRASHSCOPE's reports are easily readable and allow for reliable reproduction of crashes by presenting more explicit information than human written reports.Comment: 12 pages, in Proceedings of 9th IEEE International Conference on Software Testing, Verification and Validation (ICST'16), Chicago, IL, April 10-15, 2016, pp. 33-4